TensorFlow的中阶API主要包括各种模型层，损失函数，优化器，数据管道，特征列等等。
下面的范例使用TensorFlow2.0的中阶API实现线性回归模型和和DNN二分类模型。
本文全部内容及其源码公布在github项目eat_tensorflow2_in_30_days项中的"3-2, 中阶API示范"章节，在公众号后台回复关键字："tf", 获取项目github仓库链接。
代码语言：javascript复制import tensorflow as tf
#打印时间分割线
@tf.function
def printbar():
today_ts = tf.timestamp()%(24*60*60)
hour = tf.cast(today_ts//3600 8,tf.int32)%tf.constant(24)
minite = tf.cast((today_ts600)//60,tf.int32)
second = tf.cast(tf.floor(today_ts`),tf.int32)
def timeformat(m):
if tf.strings.length(tf.strings.format("{}",m))==1:
return(tf.strings.format("0{}",m))
else:
return(tf.strings.format("{}",m))
timestring = tf.strings.join([timeformat(hour),timeformat(minite),
timeformat(second)],separator = ":")
tf.print("=========="*8 timestring)
一，线性回归模型
1，准备数据
代码语言：javascript复制import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import tensorflow as tf
from tensorflow.keras import layers,losses,metrics,optimizers
#样本数量
n = 400
# 生成测试用数据集
X = tf.random.uniform([n,2],minval=-10,maxval=10)
w0 = tf.constant([[2.0],[-3.0]])
b0 = tf.constant([[3.0]])
Y = X@w0 b0 tf.random.normal([n,1],mean = 0.0,stddev= 2.0) # @表示矩阵乘法,增加正态扰动# 数据可视化%matplotlib inline
%config InlineBackend.figure_format = 'svg'
plt.figure(figsize = (12,5))
ax1 = plt.subplot(121)
ax1.scatter(X[:,0],Y[:,0], c = "b")
plt.xlabel("x1")
plt.ylabel("y",rotation = 0)
ax2 = plt.subplot(122)
ax2.scatter(X[:,1],Y[:,0], c = "g")
plt.xlabel("x2")
plt.ylabel("y",rotation = 0)
plt.show()
#构建输入数据管道
ds = tf.data.Dataset.from_tensor_slices((X,Y))
.shuffle(buffer_size = 100).batch(10)
.prefetch(tf.data.experimental.AUTOTUNE)
2，定义模型
代码语言：javascript复制model = layers.Dense(units = 1)
model.build(input_shape = (2,)) #用build方法创建variables
model.loss_func = losses.mean_squared_error
model.optimizer = optimizers.SGD(learning_rate=0.001)
3，训练模型
代码语言：javascript复制#使用autograph机制转换成静态图加速
@tf.function
def train_step(model, features, labels):
with tf.GradientTape() as tape:
predictions = model(features)
loss = model.loss_func(tf.reshape(labels,[-1]), tf.reshape(predictions,[-1]))
grads = tape.gradient(loss,model.variables)
model.optimizer.apply_gradients(zip(grads,model.variables))
return loss
# 测试train_step效果
features,labels = next(ds.as_numpy_iterator())
train_step(model,features,labels)
@tf.function
def train_model(model,epochs):
for epoch in tf.range(1,epochs 1):
loss = tf.constant(0.0)
for features, labels in ds:
loss = train_step(model,features,labels)
if epochP==0:
printbar()
tf.print("epoch =",epoch,"loss = ",loss)
tf.print("w =",model.variables[0])
tf.print("b =",model.variables[1])
train_model(model,epochs = 200)
# 结果可视化
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
w,b = model.variables
plt.figure(figsize = (12,5))
ax1 = plt.subplot(121)
ax1.scatter(X[:,0],Y[:,0], c = "b",label = "samples")
ax1.plot(X[:,0],w[0]*X[:,0] b[0],"-r",linewidth = 5.0,label = "model")
ax1.legend()
plt.xlabel("x1")
plt.ylabel("y",rotation = 0)
ax2 = plt.subplot(122)
ax2.scatter(X[:,1],Y[:,0], c = "g",label = "samples")
ax2.plot(X[:,1],w[1]*X[:,1] b[0],"-r",linewidth = 5.0,label = "model")
ax2.legend()
plt.xlabel("x2")
plt.ylabel("y",rotation = 0)
plt.show()
二，DNN二分类模型
1，准备数据
代码语言：javascript复制import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import tensorflow as tf
from tensorflow.keras import layers,losses,metrics,optimizers
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
#正负样本数量
n_positive,n_negative = 2000,2000
#生成正样本, 小圆环分布
r_p = 5.0 tf.random.truncated_normal([n_positive,1],0.0,1.0)
theta_p = tf.random.uniform([n_positive,1],0.0,2*np.pi)
Xp = tf.concat([r_p*tf.cos(theta_p),r_p*tf.sin(theta_p)],axis = 1)
Yp = tf.ones_like(r_p)
#生成负样本, 大圆环分布
r_n = 8.0 tf.random.truncated_normal([n_negative,1],0.0,1.0)
theta_n = tf.random.uniform([n_negative,1],0.0,2*np.pi)
Xn = tf.concat([r_n*tf.cos(theta_n),r_n*tf.sin(theta_n)],axis = 1)
Yn = tf.zeros_like(r_n)
#汇总样本
X = tf.concat([Xp,Xn],axis = 0)
Y = tf.concat([Yp,Yn],axis = 0)
#可视化
plt.figure(figsize = (6,6))
plt.scatter(Xp[:,0].numpy(),Xp[:,1].numpy(),c = "r")
plt.scatter(Xn[:,0].numpy(),Xn[:,1].numpy(),c = "g")
plt.legend(["positive","negative"]);
#构建输入数据管道
ds = tf.data.Dataset.from_tensor_slices((X,Y))
.shuffle(buffer_size = 4000).batch(100)
.prefetch(tf.data.experimental.AUTOTUNE)
2，定义模型
代码语言：javascript复制class DNNModel(tf.Module):
def __init__(self,name = None):
super(DNNModel, self).__init__(name=name)
self.dense1 = layers.Dense(4,activation = "relu")
self.dense2 = layers.Dense(8,activation = "relu")
self.dense3 = layers.Dense(1,activation = "sigmoid")
# 正向传播
@tf.function(input_signature=[tf.TensorSpec(shape = [None,2], dtype = tf.float32)])
def __call__(self,x):
x = self.dense1(x)
x = self.dense2(x)
y = self.dense3(x)
return y
model = DNNModel()
model.loss_func = losses.binary_crossentropy
model.metric_func = metrics.binary_accuracy
model.optimizer = optimizers.Adam(learning_rate=0.001)
# 测试模型结构
(features,labels) = next(ds.as_numpy_iterator())
predictions = model(features)
loss = model.loss_func(tf.reshape(labels,[-1]),tf.reshape(predictions,[-1]))
metric = model.metric_func(tf.reshape(labels,[-1]),tf.reshape(predictions,[-1]))
tf.print("init loss:",loss)
tf.print("init metric",metric)
3，训练模型
代码语言：javascript复制#使用autograph机制转换成静态图加速
@tf.function
def train_step(model, features, labels):
with tf.GradientTape() as tape:
predictions = model(features)
loss = model.loss_func(tf.reshape(labels,[-1]), tf.reshape(predictions,[-1]))
grads = tape.gradient(loss,model.trainable_variables)
model.optimizer.apply_gradients(zip(grads,model.trainable_variables))
metric = model.metric_func(tf.reshape(labels,[-1]), tf.reshape(predictions,[-1]))
return loss,metric
# 测试train_step效果
features,labels = next(ds.as_numpy_iterator())
train_step(model,features,labels)
@tf.function
def train_model(model,epochs):
for epoch in tf.range(1,epochs 1):
loss, metric = tf.constant(0.0),tf.constant(0.0)
for features, labels in ds:
loss,metric = train_step(model,features,labels)
if epoch==0:
printbar()
tf.print("epoch =",epoch,"loss = ",loss, "accuracy = ",metric)
train_model(model,epochs = 60)
# 结果可视化
fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2,figsize = (12,5))
ax1.scatter(Xp[:,0].numpy(),Xp[:,1].numpy(),c = "r")
ax1.scatter(Xn[:,0].numpy(),Xn[:,1].numpy(),c = "g")
ax1.legend(["positive","negative"]);
ax1.set_title("y_true");
Xp_pred = tf.boolean_mask(X,tf.squeeze(model(X)>=0.5),axis = 0)
Xn_pred = tf.boolean_mask(X,tf.squeeze(model(X)<0.5),axis = 0)
ax2.scatter(Xp_pred[:,0].numpy(),Xp_pred[:,1].numpy(),c = "r")
ax2.scatter(Xn_pred[:,0].numpy(),Xn_pred[:,1].numpy(),c = "g")
ax2.legend(["positive","negative"]);
ax2.set_title("y_pred");
